




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a							     =					logging.get_logger(__name__)

__a							     =					{
    'google/vivit-b-16x2-kinetics400': (
        'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''vivit'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE=224  ,_SCREAMING_SNAKE_CASE=32  ,_SCREAMING_SNAKE_CASE=[2, 16, 16]  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=768  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=3_072  ,_SCREAMING_SNAKE_CASE="gelu_fast"  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=1e-06  ,_SCREAMING_SNAKE_CASE=True  ,**_SCREAMING_SNAKE_CASE  ,)					->						int:
         UpperCAmelCase_     :				List[str]  =     hidden_size
         UpperCAmelCase_     :				Union[str, Any]  =     num_hidden_layers
         UpperCAmelCase_     :				List[str]  =     num_attention_heads
         UpperCAmelCase_     :				List[str]  =     intermediate_size
         UpperCAmelCase_     :				Tuple  =     hidden_act
         UpperCAmelCase_     :				Any  =     hidden_dropout_prob
         UpperCAmelCase_     :				Optional[Any]  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				Optional[Any]  =     initializer_range
         UpperCAmelCase_     :				Tuple  =     layer_norm_eps

         UpperCAmelCase_     :				List[str]  =     image_size
         UpperCAmelCase_     :				str  =     num_frames
         UpperCAmelCase_     :				str  =     tubelet_size
         UpperCAmelCase_     :				int  =     num_channels
         UpperCAmelCase_     :				Any  =     qkv_bias

         super().__init__(**_SCREAMING_SNAKE_CASE    )







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case : int     		=		{}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case : str     		=		["""NllbTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case : Dict     		=		["""NllbTokenizerFast"""]


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_nllb import NllbTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_nllb_fast import NllbTokenizerFast

else:
 import sys

 snake_case : Dict     		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)